Enhanced Collaborative Filtering: Combining Autoencoder and Opposite User Inference to Solve Sparsity and Gray Sheep Issues

• Published in: Computers (Basel) 2024-11, Vol.13 (11), p.275
• Main Authors: El Youbi El Idrissi, Lamyae, Akharraz, Ismail, El Ouaazizi, Aziza, Ahaitouf, Abdelaziz
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; autoencoder; Collaboration; collaborative filtering; Computational linguistics; Datasets; Decomposition; Deep learning; Equipment and supplies; Error analysis; Filtration; gray sheep; Language processing; Machine learning; Methods; Natural language interfaces; Neural networks; Ratings & rankings; recommender system; Recommender systems; Root-mean-square errors; Sheep; Social networks; Sparsity; Teaching; User behavior
• ISSN: 2073-431X; 2073-431X
• DOI: 10.3390/computers13110275

In recent years, the study of recommendation systems has become crucial, capturing the interest of scientists and academics worldwide. Music, books, movies, news, conferences, courses, and learning materials are some examples of using the recommender system. Among the various strategies employed, collaborative filtering stands out as one of the most common and effective approaches. This method identifies similar active users to make item recommendations. However, collaborative filtering has two major challenges: sparsity and gray sheep. Inspired by the remarkable success of deep learning across a multitude of application areas, we have integrated deep learning techniques into our proposed method to effectively address the aforementioned challenges. In this paper, we present a new method called Enriched_AE, focused on autoencoder, a well-regarded unsupervised deep learning technique renowned for its superior ability in data dimensionality reduction, feature extraction, and data reconstruction, with an augmented rating matrix. This matrix not only includes real users but also incorporates virtual users inferred from opposing ratings given by real users. By doing so, we aim to enhance the accuracy of predictions, thus enabling more effective recommendation generation. Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models.